Understanding how interfacial water governs photocatalytic hydrogen evolution is essential for rational photocatalyst design. Here, we directly characterize the adsorption state and hydrogen-bonding structure of interfacial water and correlate them with hydrogen evolution activity. We reveal that relatively weaker water-TiO₂ interactions and more flexible hydrogen-bond networks favor higher interfacial water reactivity. These findings provide a molecular basis for engineering water-catalyst interfaces to enhance photocatalytic performance.

A Japanese research team discovered that giant-nucleus cells survive iron-induced oxidative stress and resist ferroptosis. These cells, which become more common with BRCA1 mutations, could signal the beginning of early-stage cancer.

An international team of researchers has developed a protein language model that unites information from protein sequences and structures within a single representation. The new model, CLSS, produces detailed maps of relationships across the protein universe and can accurately represent both full-length protein domains and their pieces. The approach could help researchers uncover distant evolutionary relationships between proteins and better understand how today's enormous diversity of proteins emerged.

Study finds that children aged 8-12 diagnosed with attention deficit hyperactivity disorder (ADHD) retell stories less completely and coherently than their neurotypical counterparts, potentially putting them at a disadvantage in social and educational settings. Visual aids help narrow the gap, but more attention should be given to children’s narrative ability to create more supportive education and social environments for children with ADHD-associated difficulties.

The book, titled Theatre at Messene: An Architectural Study has been awarded the prestigious Herend Award, and the volume offers a comprehensive architectural study tracing the evolution of Greek into Roman theatre architecture and ancient engineering practices.

It was not fully understood how deep-sea microorganisms maintain their structure and function under high-pressure conditions. A research team from Kyushu University, conducted an experimental study focusing on PoXeR, a protein from a deep-sea bacterium. As increasing pressure was applied to the protein, the researchers found that it assembled itself into groups of three, called a trimeric structure to stabilize itself. This adaptive strategy revealed how deep-sea proteins remain stable under extreme pressure.

Previous studies may have underestimated how much Mercury has contracted as the planet cooled over billions of years because its rough surface hides some of the geological evidence.

Electric fields can reliably reverse magnetization in nanoscale structures of the multiferroic material BiFe_0.9Co_0.1O₃ (BFCO), as reported by researchers from Science Tokyo. Multiferroic materials display coupled electric and magnetic order, making them attractive for low-power memory devices. The team fabricated 190 nm BFCO nanodots and used advanced imaging techniques to directly observe the restructured electric polarization arrangement upon applying an electric field, driving a persistent reversal of magnetization.

Thorium-229, a promising basis for nuclear clocks, has a rare nuclear transition that can be excited with laser light. However, nuclei embedded in crystals are affected by their local environments. Now, researchers have used laser Mössbauer spectroscopy to identify four distinct sites occupied by thorium-229 in calcium fluoride crystals. The team characterized their electric field environments, measured site-selective properties, and identified microscopic structures for the two dominant configurations, providing information for designing solid-state nuclear clocks. 

Researchers have developed an AI-based system that automatically and objectively detects the moment when fish experience loss of equilibrium (LOE) due to temperature stress. The system combines DeepLabCut, a deep-learning AI that captures animal posture from video, with ResNet34, a deep-learning AI-based image-classification technology. Their system is expected to help predict the effects of climate change on fish. The research team was led by Specially Appointed Lecturer Tomoya Nakayama and Yoshiya Matsuo (a master’s student at the time) of the Institute of Transformative Bio-Molecules (WPI-ITbM) at Nagoya University, in collaboration with Associate Professor Tatsuto Hasegawa and Takuya Kato (a master’s student at the time) of the University of Fukui. Their research was published in the journal Scientific Reports on September 10, 2026.